Over the past decade or so, expenditure on outpatient prescription
drugs has become one of the fastest-growing components of health care
expenditure in the U.S. [11]. Much of the debate over the Medicare
Catastrophic Coverage Act of 1988 and the current debate over health
care reform is concerned with the rising cost and utilization of
prescription drugs particularly among the elderly. There is of course a
general debate over the extent to which the problems of adverse
selection and/or moral hazard arising from universal coverage will
render futile the cost estimates for the sundry forms of health
insurance to be covered under any reform. The case of prescriptions is
particularly interesting, as witness the pressure placed on
pharmaceutical companies by administration officials in 1993.

This paper analyzes the influence that health insurance has on
elderly individuals' decisions to use prescription drugs. We create
a data base from a survey of health insurance and medicine use in the
Commonwealth of Pennsylvania conducted during the summer of 1990 by
researchers affiliated with the Medicine, Health, and Aging Project at
Penn State [23]. Pennsylvania is particularly interesting in this regard
because of the generous provisions of the PACE (Pharmaceutical
Assistance Contract for the Elderly) program, which pays for all
outpatient prescriptions for low income elderly, less a $4.00 copayment per 30 days dosage.

Our analytic framework is dictated by three considerations. First, we
wish to produce quantitative estimates of insurance effects over a wide
range of prescription benefit provisions. Most prior research on
prescription drug demand has focused on the effects of relatively minor
alterations in insurance benefits such as the addition of a $.50 or
$1.00 copayment. Findings from these studies are not generalizable to
situations in which individuals gain (or lose) prescription coverage or
face other major changes in prescription benefits.

Second, we wish to address the question of whether proscription
demand is driven more by the own-price effects of drug coverage or by
the cross-price effects of Medicare supplementation for ambulatory
physician visits. Outpatient physician visits are covered under Medicare
Part B, but are subject to deductible and coinsurance provisions which
may reduce their use. Since physicians control access to prescription
medicine, it follows that changes in the price of physician care may
affect drug demand as well.

The third consideration is methodological rather than substantive.
Elderly who have prescription coverage obtain it from three basic
sources: employer-sponsored plans, individual Medigap policies, or
public programs including Medicaid and pharmaceutical assistance plans
like PACE. Except for employer-sponsored plans, the elderly must seek
coverage on their own. It is reasonable to suspect that those who do are
more likely to need prescription drugs than those who do not seek
coverage. Unless controlled for, self selection can lead to serious bias
in models designed to measure the moral hazard engendered by insurance.

Most of the empirical research on the demand for prescription drugs
involve studies of differences in utilization following small changes in
insurance coverage. Several studies conducted in the 1960s and early
1970s concluded that Medicaid copayments reduce drug utilization rates
[7; 2; 21], but methodological shortcomings make the results difficult
to generalize. Later studies using interrupted time-series designs have
produced similar findings. A South Carolina study observed a significant
decline in drug use following imposition of a $.50 drug copayment in
that state's Medicaid program in 1977 [12; 17]. A study by Soumerai
et al. [22] found significant reductions in prescription fill rates
among New Hampshire Medicaid recipients following imposition of a
three-per-month [R.sub.x] limit and a $1.00 copayment.

Similar before-and-after studies (some with controls, some without)
have been conducted in Great Britain following changes in National
Health Service patient cost-sharing provisions for prescription drugs
that occurred at various times from 1969 through 1986 [16; 20; 1; 8; 14;
19]. Estimated [R.sub.x] price elasticities from these studies range
from a low of -.06 [19] to a high of -.64 [14], with a preponderance of
estimates in the -.10 to -.20 range.

Changes in cost-sharing for prescription medicine among privately
insured groups in the United States have also been shown to have
significant effects on drug utilization in the expected direction. An
early study by Weeks [25] found that the introduction of prepaid drug
benefits in an employee health plan increased the average number of
prescriptions filled. A recent study by Harris, Stergachis and Ried [6]
discovered that progressively higher copayment levels led to
proportionate reductions in drug use among non-aged HMO enrollees.

The Rand Health Insurance Experiment (HIE) produced two papers on the
relationship between drug utilization and insurance coverage [10; 9].
Unfortunately, the health insurance packages offered to HIE participants
did not vary according to prescription drug benefits, so that it proved
impossible to estimate directly the price effect of [R.sub.x] coverage
on utilization levels. Moreover, the sample frame excluded the elderly.
Despite these shortcomings, the HIE provides important clues to the
possible impact of insurance on medicine use. In their 1985 paper,
Leibowitz, Manning, and Newhouse [10] examined the relationship between
[R.sub.x] utilization and patient cost-sharing (for all medical
services, not just drugs). They found that HIE enrollees with generous
insurance filled significantly more prescriptions than did those with
less generous coverage; in fact, the degree of price responsiveness did
not differ much between drugs and other medical services. Their
conclusion that "drugs, like medical care expenditures in general,
respond to cost-sharing faced by consumers" [10, 1068] has been
widely debated on grounds that the HIE results cannot distinguish
between the own-price effect of insurance on the covered service in
question (prescription drugs) and the cross-price effect of coverage for
services that complement drug therapy (physician visits). A second
Leibowitz paper [9] reported no significant relationship between
insurance plan generosity and utilization rates for over-the-counter
medicine (which may potentially substitute for [R.sub.x] products).

Finally, we take note of a study by Cameron et al. [3] which uses
Australian data to estimate a series of health services demand equations
including equations for prescription drug use. This study is relevant to
our purposes mainly because it demonstrates the difficulty of estimating
insurance effects in the presence of self-selected coverage (variation
in own-price for prescribed medicine in Australia arises from private
insurance supplements of the national insurance plan). The authors first
estimate a model with endogenous insurance variables and find that more
generous insurance coverage has a positive and significant effect on
prescription utilization. They then reestimate the models using
instrumental variables for the insurance options and find implausibly
large insurance coefficients and a sign reversal (persons subject to a
$2.00 prescription copayment are predicted to use nearly double the
number of prescribed medicines compared to those for whom drugs are
free).

II. Empirical Model

The estimation of insurance effects on the demand for health care in
choice-based samples is difficult because the determinants of insurance
coverage are strongly correlated with the determinants of use. Persons
with poor health have higher utilization rates, but are also likely to
purchase larger amounts of insurance. For the analyst this creates a
modeling problem since the insurance variables will capture both the
demand-inducing effect of better coverage and residual effects
associated with insurance selection. To make matters worse, the standard
econometric remedies for endogeneity - instrumental variables and
Heckman selection-control models - perform poorly in this context.
Experience similar to Cameron's has been reported by Newhouse and
Phelps [13] and others. A recent Monte Carlo experiment reported by
Hartman [6] shows that two-stage estimators are inherently unstable. The
problem appears to arise from the fact that the instrument created in
the first-stage stage equation is highly collinear with the exogenous
variables in the second stage. The coefficient estimates produced in the
second stage are thus extremely sensitive to minor changes in the
specification of either equation.

Our approach to modeling the demand for prescription drugs controls
for self-selectivity in another way. It is a variation of a longitudinal
model developed by Wolfe and Goddeeris [26] in which past utilization is
used to proxy for the unobserved determinants of insurance choice. We
test for the endogeneity of insurance choice using Hausman tests and
find that insurance choice is correlated with the error term when our
proxy ms excluded and uncorrelated when it is included. Thus the Hausman
test is able to discriminate between the two models, and justifies, to
that extent, the use of the designated proxies. On that basis we can
measure the moral hazard associated with insurance coverage directly
from the insurance coefficients.

Formally, we propose a model of prescription demand of the following
sort,

where M is the number of prescription drugs reported purchased by the
ith individual within a certain time period (t), X is a vector of
personal characteristics, and I is a vector of insurance dummies.
Although [[Beta].sub.2] captures the moral hazard effect, OLS estimates
of equation (1) are biased because the error term is of the form:

[e.sub.it] = [v.sub.it] - [h.sub.it] (2)

where v is random noise and h is health status over the period in
which the insurance is in force. The value of [h.sub.it] is not directly
observable until after the insurance decision has been made but is
presumed by the individual (and the modeler) to be related to
[h.sub.it-1]. The better the health the fewer the M, but also the less
likely is insurance. Hence I is correlated with the error term.

Wolfe and Goddeeris deal with this difficulty by assuming that h is
first-order autoregressive. Performing the Cochrane-Orcutt
transformation yields an equation in which lags of all the variables are
included on the right hand side and the error term consists only of
[v.sub.it] and the residual from the AR(1) model of [h.sub.it] and hence
is uncorrelated with the regressors.

This model is a specific example in the class of models where health
status is forecasted linearly from some information set Z:

E([h.sub.it][where][Z.sub.it-1]) = [[Beta].sub.3][Z.sub.it-1] (3)

where Z is the set of information available to the ith individual at
time t - 1. In the above scenario, Z consists of all lagged variables,
but because of the autocorrelation, [[Beta].sub.3] is constrained. Our
data set, described in the next section, is limited in that we do not
have access to all the variables at time t - 1 (specifically, we do not
have longitudinal data on prescription drug use). We do, however, have
data on a number of health indicators which we believe will encompass
the information available at time t - 1. Because we eschew the
parameterization suggested by the Cochrane-Orcutt transformation, the
estimates of [[Beta].sub.3] are unconstrained.

where u is the prediction error on h. Under the current error
structure, the estimate of [[Beta].sub.2] is consistent.

We can test the appropriateness of equation (4) via a Hausman test.
We run this regression without [Z.sub.t-1] and use Hausman procedures to
test for endogeneity of the insurance choice variables. It will turn out
(as described below) that exogeneity can be rejected, and from this we
infer that the difficulties discussed above indeed exist. We then add
[Z.sub.t-1] to the list of regressors and reperform the test, which in
this case cannot reject exogeneity. Hence the Hausman test is able to
discriminate between the two specifications and leads to our belief that
the expanded model provides consistent estimates.

We estimate three versions of equation (4). First is an OLS model
with observations on prescription drug use for all individuals
(including those with no reported use). Second is a Probit probability-of-use model with prescription utilization coded as a binary
variable (1 = reports use of prescription drugs; 0 = reports no drug
use). Third is an OLS level-of-use equation estimated over the subset of
individuals reporting one or more prescription during the study period.

III. Data

Our data set is a panel constructed from survey responses and
Medicare claims records for 4066 elderly Pennsylvanians who were asked
about their demographic characteristics, current health status,
insurance coverage and medicine use in a mail questionnaire administered
in the late summer and early fall of 1990. The survey sample was
randomly selected from Medicare enrollment files for Pennsylvania.
Disabled beneficiaries under age 65 were excluded from the sample frame
as were elderly HMO enrollees.(1) The survey questionnaire was sent to
6,502 persons. After reminders and repeat mailings 4,508 responded (69.3
percent). Further detail on the survey instrument and its implementation
is contained in Stuart et al. [23].

We selected calendar year 1988 as the t - 1 period for the Z
variables. Thus when constructing the analytical file for the study we
eliminated 442 individuals who were not covered for Medicare Parts A and
B continuously from January, 1988 through the survey date. The
characteristics of the reduced sample are shown in Table I. Respondents
reported an average of 1.23 prescriptions or prescription refills during
a two-week recall period. Approximately half (54 percent) filled at
least one prescription and among that group use averaged two-and-a-half
[R.sub.x] scripts. These utilization rates are considerably above those
reported in the most current National Medical Expenditure Survey [11],
but then the percentage of elderly Pennsylvanians with prescription
coverage is also significantly above the national mean [23].

The X vector is represented by various demographic characteristics
including age, sex, race, education, annual income, and marital status.
The income breakdown includes two categories used to identify
individuals putatively eligible for public programs, Medicaid (income
[less than] $6,000) and PACE (income [less than] $12,000 if single,
[less than] 15,000 if married). The next income bracket ($12,000 to
$15,000) is limited to single persons given the PACE restriction just
noted. The X vector also contains measures of self-reported health
status contemporaneous to the survey date. These included general
questions about physical and mental health, questions about specific
health conditions (here summarized into two measures), and queries
regarding smoking and drinking behavior.

As noted, all respondents are Medicare beneficiaries. They supplement
Medicare in several ways including Medicaid, PACE, and private insurance
plans. We characterized the latter according to source of coverage
(employer-sponsored and individual Medigap plans), type of benefit
(ambulatory physician visits and prescription drugs), and single or
multiple plans. Individuals may be included in more than one category.
In fact most are.

The survey did not elicit information on the scope of drug benefits
provided in the private plans. Medicaid represents the most
comprehensive drug coverage on the list. During the study period elderly
Medicaid recipients were charged a $.50 copayment for certain classes of
drugs, but most prescription medicine was free. PACE imposed a $4.00
copayment per prescription or prescription refill (both limited to
dosages of 30 days). The modal employer-sponsored plan included
prescription benefits of some type. Few individual Medigap policies
provide this coverage. Most private plans that supplement Medicare Part
B coverage of physician visits pay the Medicare 20 percent coinsurance;
few cover the Part B deductible or excess charges above Medicare limits.

The [Z.sub.t-1] vector captures prior health status and is proxied by
1988 Medicare utilization. We had a wide choice of Medicare variables
for this purpose and selected two, days hospitalized in 1988 and total
Medicare Part A payments for that year.

The final set of model variables represent health care resources
available to survey respondents. These include measures of physician,
hospital, nursing home, and pharmacy supply relative to county
population levels. Data for these variables were obtained from the Area
Resource File and the Pennsylvania Department of Health and were linked
to the person-level analytical file via respondent addresses and the
county FIPS code.

[TABULAR DATA FOR TABLE I OMITTED]

IV. Results

We are interested in the effects that the six types of insurance have
on the use of pharmaceuticals. Enrollment in employer-sponsored plans is
considered exogenous in all models, leaving us with five potential
endogenous insurance dummies. Our first task is to use the Hausman test
to deliver models free of endogeneity problems. The Hausman test
consists of two steps (see e.g., Godfrey [4]). First, five probit
regressions are run with the insurance dummies as dependent variables.
The residuals from these regressions are collected and added as
covariates to the utilization equation (with the insurance choice
dummies also included). If the coefficients of the residuals are
significantly different from zero, exogeneity is rejected.

Following this procedure, the probits were estimated, using as
right-hand side variables all of the variables discussed above. Also
included, to aid in identification, were county-specific measures of
rurality.(2) The residuals from these models were then inserted into the
prescription utilization equations containing the prior utilization
([Z.sub.t-1]) measures and those without them. We rejected exogeneity in
every model that did not contain the [Z.sub.t-1] measures. For example,
in the version of the first model (use estimated over the entire sample)
that contained no [Z.sub.t-1] variables, the F-test for the coefficients
on the residuals was 9.307 (p [less than] .0001). We conclude that
exogeneity is rejected at any conventional level of significance. When
this same model is estimated with the [Z.sub.t-1] variables included,
the F-statistic was 0.2015 with a p-value of 0.96. Exogeneity in this
case is substantively accepted. The Hausman test has discriminated
between the two versions of the utilization model and the [Z.sub.t-1]
measures are shown to be jointly adequate for the representation of
unobserved determinants of insurance choice.(3)

To then estimate the selection-purged models, we run the regressions
with the auxiliary regressors (the probit residuals) removed. Our
findings are presented in Table II.

Overall, the models perform quite well, explaining between 15 and 20
percent of the variance in prescription use among respondents. Many of
the individual variables, however, have very low significance. These
include ethnicity, educational attainment, income,(4) marital status,
mental health rating, and health care resources in the county. By
contrast, all of the indicators of current physical health are
significant in both the full-sample utilization equation (column 1) and
the probability of use model (column 2). Moreover, the magnitudes of the
health status indicators are in accord with expectations: people in
better (self-perceived) health use fewer medications. The prior
utilization [Z.sub.t-1] measures also have the expected signs and are
statistically significant in two of the three models.

Among the background variables are three idiosyncratic coefficients:
alcohol users, smokers, and age all have negative coefficients when
positive might have been expected. In the case of age, the result is not
entirely unexpected given the evidence of morbidity compression found in
our month-to-death models described in Stuart et al. [23]. If very old
survivors are healthier than their younger counterparts, then on average
older people within any given cross-section will use fewer prescription
drugs (even when the time trend for any individual is positive). The
signs on the other two variables are unexpected. The highly significant
negative effects of drinking and [TABULAR DATA FOR TABLE II OMITTED]
smoking on the probability of filling any prescription is particularly
problematic. It seems unlikely that these behaviors would have
protective effects on health (although there is some evidence that light
drinking reduces risk of heart attacks). Perhaps instead these
coefficients reflect a general recklessness towards health.

We are, of course, primarily interested in the coefficients of the
insurance dummies. The most striking result is for PACE enrollees.
According to the full-sample estimate (column 1) PACE beneficiaries fill
0.29 more prescriptions or refills per two-week period than do elderly
who are not covered by an employer-sponsored plan and have neither
prescription coverage nor Medicare supplementation for ambulatory
physician visits (the excluded category for the insurance vector). Since
the model has been putatively purged of adverse selection bias, we
conclude that this is the result of the price subsidy that PACE
beneficiaries enjoy.

The results for other insurance and subsidy programs are less
substantive and less precise. In the full-sample estimates, private
coverage for ambulatory physician care and prescription drugs also
increase pharmaceutical use by about one-tenth of a prescription in a
two-week period, although the test statistics indicate only borderline significance. Employer-sponsored insurance appears to have a negative
effect on prescription use which is most notable in the
probability-of-use equation (column 2). This anomaly is more apparent
than real since prescription and physician coverage effects are
independently assessed. The negative coefficients on [R.sub.x] use among
those with employer-based coverage may reflect some residual health
status effects associated with previous employment. It is unlikely that
these findings are due to usage restrictions or drug review imposed by
employers for the simple reason that such utilization control programs
were not common during the study period. The failure to find significant
effects associated with multiple Medigap coverage should be interpreted
in the same light as the employment insurance coefficients. However, we
are surprised by the consistently negative (albeit insignificant) signs
on the Medicaid coverage variable. Medicaid offers the most generous
package of prescription benefits available to elderly Pennsylvanians and
it would be perverse in the extreme for recipients to reduce their use
of prescribed medicines because of the benefit. In all likelihood, these
coefficients are an artifact of some other characteristic of Medicaid
enrollment not captured in the model.

Further insight into the effect of insurance on drug utilization can
be gained by comparing the probability-of-use and level-of-use-by-users
results in columns 2 and 3 of Table II. Here it becomes evident that
insurance does not simply shift the demand curve outward. Rather the
demand-inducing effect appears to be limited to those who would not use
any prescription medicine in the absence of insurance. For example, at
mean values for the background variables, enrollment in PACE raises the
probability of prescription use by 11 percent while ambulatory physician
coverage raises it by 5 percent. In both cases the effects are
significant at p [less than] .001. By contrast, neither form of coverage
has a statistically significant effect on the number of medicines used
by persons filling one or more prescriptions. Indeed, none of the
insurance dummies was significant in the use-by-users equation.

These findings shed new light on the demand for prescription drugs by
the elderly. It appears clear that drug use is price sensitive both to
direct subsidies and to coverage of complementary physician services.
The actual degree of price sensitivity is impossible to compute for most
of the insurance variables because of heterogeneous benefits within the
coverage groups (even Medicaid recipients face different out-of-pocket
prices depending on the drugs prescribed). PACE is an exception. During
the study period PACE imposed a flat $4.00 copay per prescription or
refill regardless of the actual charge. Between July and December 1990
(when the survey was fielded), the average usual and customary charge
per PACE prescription was $24.98 [15]. Most pharmacies offer elderly
patrons a standard 10 percent "senior citizen discount" which
would reduce the average noninsured prescription charge to $22.48. The
average subsidy is thus $18.48 or 82.2 percent. Based on our full-sample
regression results, PACE beneficiaries responded to this price reduction
with a 27.6 percent increase in the quantity of prescriptions
purchased,(5) thus yielding an own-price elasticity of -.34.

V. Concluding Remarks

Whether this elasticity estimate can be generalized beyond the PACE
program is an open question. We are confident that the PACE coefficients
truly reflect price sensitivity and not adverse selection, but they
reflect it within the context of a particular program with unique
features. PACE differs from most private Medicare supplements, whether
employer-sponsored or individually-purchased, in that the program
reimburses pharmacies directly. Some authors have conjectured that
"card programs" (like PACE) induce greater demand than
indemnity plans with similar benefit coverage because there is no need
to pay up front and beneficiaries are protected from the risk of lost or
misplaced receipts [18]. PACE also differs from other public-sector
pharmaceutical assistance plans and state Medicaid programs in that it
places few administrative restrictions on drug benefits, with one
notable exception. There is no restrictive formulary. Both branded and
generic products are covered. There are no limits on the number of
prescriptions that can be filled at one time nor are there limits on
refills. However, coverage is limited to one-month's supply at a
time; each additional month's supply is subject to another copay.

This last feature both lowers the value of the PACE subsidy for
maintenance drugs and makes it appear as if program beneficiaries are
refilling more prescriptions than are individuals whose prescriptions
provide two or three month's supply. Because our survey did not
distinguish between initial prescriptions and refills, we cannot rule
out the possibility that some of the higher demand attributed to the
PACE price subsidy is really an artifact of this coverage restriction.
To the extent this bias is present in our results it should be most
evident in the use-by-user equation, since there are the people most
likely to be refilling maintenance prescriptions. We simply note that
the PACE coefficient in this equation is small and insignificant whereas
the PACE coefficient in the probability-of-any-prescription-fill
equation is large and highly significant.

Caveats aside, the results of this analysis strongly suggest that if
prescription drug coverage for the elderly were improved - say through
an expansion of Medicare benefits - that the demand for drugs would
increase. But by how much? Surely by less than a doubling of drug
expenditures on behalf of the previously uninsured, as recently
projected by Waldo [24]. Nor is the impact likely to be as large as the
3.4% increase per 10% decline in out-of-pocket expense predicted by our
own PACE coefficients (for reasons described above). The importance of
our findings lies less in the point estimates than in the ability of the
estimates to show that higher utilization rates are due to the insurance
coverage itself and are not just an artifact of adverse selection.

1. Medicare HMO enrollees were excluded because HCFA does not
maintain complete utilization and payment records for them.

2. The rurality measure is the 10-level Human Resource Profile County
Adjacency Code classification scheme compiled by the Health Resources
Administration and available on the Area Resource File.

3. A precisely analogous pair of results is obtained using the linear
probability model in the first stage.

4. The coefficients on the categorical income variables capture the
effect of income net of insurance coverage. As expected, the price
effects of insurance - particularly the comprehensive coverage afforded
by Medicaid and PACE - are stronger than income effects on prescription
demand.

5. The percentage quantity increase is obtained by dividing the PACE
coefficient (0.29) by the intercept (1.05).

References

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Charges and Consumption in Groups Not Exempt From Charges." Journal
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10. -----, W. Manning, and J. Newhouse, "The Demand for
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11. Moeller, J. and N. Mathiowetz, Prescribed Medicines: A Summary of
Use and Expenditures by Medicare Beneficiaries. Washington, D.C.:
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12. Nelson, A., C. Reeder, and W. Dickson, "The Effect of a
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13. Newhouse, Joseph, and C. Phelps. "New Estimates of Price and
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14. O'Brian, B., "The Effect of Patient Charges on the
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